用循环中的大型语言模型设计多样化和高性能蛋白质
Carlos A Gomez-Uribe1, Japheth Gado1, Meiirbek Islamov1
1Solugen, Inc., Houston, Texas, United States of America.
PLoS computational biology
|June 5, 2025
概括
本研究引入了用于蛋白质工程的机器学习,使用Seq2Fitness和为多样化和自适应序列采样 (BADASS) 进行双相回火来设计具有增强适应性的新型蛋白质序列.
科学领域:
- 蛋白质工程是指蛋白质的工程.
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 定向进化对于蛋白质工程至关重要,但通常受到采样效率的限制.
- 准确预测蛋白质健身景观并有效探索它们是关键的挑战.
研究的目的:
- 开发一种用于蛋白质工程的新型机器学习方法.
- 通过改进的健身预测和景观探索来增强序列设计.
主要方法:
- 介绍了Seq2Fitness,这是一个半监督的神经网络模型,使用蛋白质语言模型进行健身预测.
- 开发了用于多样化和自适应序列采样 (BADASS) 的双相回火,用于序列探索的优化算法.
- 集成的Seq2Fitness和BADASS用于全面的蛋白质序列设计管道.
主要成果:
- 对于未见的突变,Seq2Fitness与实验适应性 (0.55对0.34) 的相关性得到改善.
- 比起竞争的方法,BADASS产生了比较高的适应性和多样化的序列,计算成本更低.
- 100%的顶级BADASS识别的序列超过了野生类型的适应性,超过了其他方法.
结论:
- 结合Seq2Fitness和BADASS的方法显著推进了蛋白质序列设计.
- 这种方法为产生各种应用的高性能蛋白质提供了一个强大的工具.
- BADASS显示了对蛋白质以外的其他基于序列的设计问题的概括潜力.
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